%1 = "tfl.mul"(%0, %arg2) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

        "//tensorflow/lite/toco:toco_tooling",
        "//tensorflow/lite/toco:tooling_util",
        "//tensorflow/lite/toco:types_proto_cc",
        "//tensorflow/lite/toco/logging:conversion_log_util",
        "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc",
        "//third_party/python_runtime:headers",  # build_cleaner: keep; DNR: b/35864863
        "@com_google_absl//absl/container:flat_hash_set",

#include "tensorflow/compiler/mlir/tensorflow/translate/mlir_roundtrip_flags.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

// Load a TF model from a GraphDef definition or a TF control flow dialect MLIR

      }
      total_count += input_type.getNumElements();
    }
    *count = total_count;
    return true;
  }

  // For conv2d/depthwise_conv/fully_connected ops.
  // This algorithm actually comes from TOCO tooling_util.cc
  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,
                                                           int64_t* count) {
    auto weight = op->getOperand(1);

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/utils/utils.h"

namespace mlir {
namespace TFL {
namespace tac {

// Returns true if 'op' is non const op. Returns false otherwise or if
// 'op' is null.

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/subgraph.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/subgraph.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

// This pass is used to fold tfl.const ops to each subgraph (func::FuncOp):
// See the example below:

#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/OwningOpRef.h"  // from @llvm-project
#include "mlir/Parser/Parser.h"  // from @llvm-project

namespace mlir {
namespace TFL {
namespace tac {

// Import the file as mlir module, the input maybe flatbuffer or mlir file.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ImportFlatbufferOrMlir(
    const std::string& input_filename, bool input_mlir,

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/mlir/lite/experimental/tac/utils/utils.h"

#include <memory>
#include <string>
#include <utility>
#include <vector>

#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_format.h"

    'mlir-hlo-opt',
    'mlir-opt',
    'mlir-tflite-runner',
    'mlir-translate',
    'odml-to-stablehlo-opt',
    'odml_to_stablehlo',
    'odml-converter',
    'stable-quant-opt',
    'tac-opt-all-backends',
    'tac-translate',
    'tf-mlir-translate',
    'tf-opt',
    'tf-quant-opt',
    'tf-reduce',
    'tf-tfrt-opt',
    'tf_tfjs_translate',
    'tf_tfl_translate',
    'tfcompile',